Evolutionary history reconstruction for Mammalian complex gene clusters

Molecular evolution of the hemoglobin gene family across vertebrates

Adaptation to various altitudes and oxygen levels is a major aspect of vertebrate evolution. Hemoglobin is an erythrocyte protein belonging to the globin superfamily, and the α-, β-globin genes of jawed vertebrates encode tetrameric ((α₂β₂) hemoglobin, which contributes to aerobic metabolism by delivering oxygen from the respiratory exchange surfaces into cells. However, there are various gaps in knowledge regarding hemoglobin gene evolution, including patterns in cartilaginous fish and the roles of gene conversion in various taxa. Hence, we evaluated the evolutionary history of the vertebrate hemoglobin gene family by analyses of 97 species representing all classes of vertebrates. By genome-wide analyses, we extracted 879 hemoglobin sequences. Members of the hemoglobin gene family were conserved in birds and reptiles but variable in mammals, amphibians, and teleosts. Gene motifs, structures, and synteny were relatively well-conserved among vertebrates. Our results revealed that purifying selection contributed substantially to the evolution of all vertebrate hemoglobin genes, with mean d_N/d_S (ω) values ranging from 0.057 in teleosts to 0.359 in reptiles. In general, after the fish-specific genome duplication, the teleost hemoglobin genes showed variation in rates of evolution, and the β-globin genes showed relatively high ω values after a gene transposition event in amniotes. We also observed that the frequency of gene conversion was high in amniotes, with fewer hemoglobin genes and higher rates of evolution. Collectively, our findings provide detail insight into complex evolutionary processes shaping the vertebrate hemoglobin gene family, involving gene duplication, gene loss, purifying selection, and gene conversion.

Fast computation of a string duplication history under no-breakpoint-reuse

In this paper, we provide an O(n log(2) n log log n log* n) algorithm to compute a duplication history of a string under no-breakpoint-reuse condition. The motivation of this problem stems from computational biology, in particular, from analysis of complex gene clusters. The problem is also related to computing edit distance with block operations, but, in our scenario, the start of the history is not fixed, but chosen to minimize the distance measure.

Revealing mammalian evolutionary relationships by comparative analysis of gene clusters

Many software tools for comparative analysis of genomic sequence data have been released in recent decades. Despite this, it remains challenging to determine evolutionary relationships in gene clusters due to their complex histories involving duplications, deletions, inversions, and conversions. One concept describing these relationships is orthology. Orthologs derive from a common ancestor by speciation, in contrast to paralogs, which derive from duplication. Discriminating orthologs from paralogs is a necessary step in most multispecies sequence analyses, but doing so accurately is impeded by the occurrence of gene conversion events. We propose a refined method of orthology assignment based on two paradigms for interpreting its definition: by genomic context or by sequence content. X-orthology (based on context) traces orthology resulting from speciation and duplication only, while N-orthology (based on content) includes the influence of conversion events. We developed a computational method for automatically mapping both types of orthology on a per-nucleotide basis in gene cluster regions studied by comparative sequencing, and we make this mapping accessible by visualizing the output. All of these steps are incorporated into our newly extended CHAP 2 package. We evaluate our method using both simulated data and real gene clusters (including the well-characterized α-globin and β-globin clusters). We also illustrate use of CHAP 2 by analyzing four more loci: CCL (chemokine ligand), IFN (interferon), CYP2abf (part of cytochrome P450 family 2), and KIR (killer cell immunoglobulin-like receptors). These new methods facilitate and extend our understanding of evolution at these and other loci by adding automated accurate evolutionary inference to the biologist's toolkit. The CHAP 2 package is freely available from http://www.bx.psu.edu/miller_lab.

The evolution of the tape measure protein: units, duplications and losses

BACKGROUND

A large family of viruses that infect bacteria, called phages, is characterized by long tails used to inject DNA into their victims' cells. The tape measure protein got its name because the length of the corresponding gene is proportional to the length of the phage's tail: a fact shown by actually copying or splicing out parts of DNA in exemplar species. A natural question is whether there exist units for these tape measures, and if different tape measures have different units and lengths. Such units would allow us to retrace the evolution of tape measure proteins using their duplication/loss history. The vast number of sequenced phages genomes allows us to attack this problem with a comparative genomics approach.

RESULTS

Here we describe a subset of phages whose tape measure proteins contain variable numbers of an 11 amino acids sequence repeat, aligned with sequence similarity, structural properties, and simple arithmetics. This subset provides a unique opportunity for the combinatorial study of phage evolution, without the added uncertainties of multiple alignments, which are trivial in this case, or of protein functions, that are well established. We give a heuristic that reconstructs the duplication history of these sequences, using divergent strains to discriminate between mutations that occurred before and after speciation, or lineage divergence. The heuristic is based on an efficient algorithm that gives an exhaustive enumeration of all possible parsimonious reconstructions of the duplication/speciation history of a single nucleotide. Finally, we present a method that allows, when possible, to discriminate between duplication and loss events.

CONCLUSIONS

Establishing the evolutionary history of viruses is difficult, in part due to extensive recombinations and gene transfers, and high mutation rates that often erase detectable similarity between homologous genes. In this paper, we introduce new tools to address this problem.